Conjoint experiments enjoy increasing popularity in political and social science, but there is a paucity of research on respondents' underlying decision-making processes. We leverage eye-tracking methodology and a conjoint experiment, administered to a subject pool consisting of university students and local community members, to examine how respondents process information when completing conjoint surveys. Our study has two main findings. First, we find a positive correlation between attribute importance measures inferred from the stated choice data and attribute importance measures based on eye movement. This validation test supports the interpretation of common conjoint metrics, such as Average Marginal Component Effects and marginal R^2 values, as valid measures of attribute importance. Second, when we experimentally increase the number of attributes and profiles in the conjoint table, respondents on average view a larger absolute number of cells but a smaller fraction of the total cells displayed, and the patterns in which they search between cells change conditionally. At the same time, however, their stated choices remain remarkably stable. This overall pattern speaks to the robustness of conjoint experiments and is consistent with a bounded rationality mechanism. Respondents can adapt to complexity by selectively incorporating relevant new information to focus on the important attributes, while ignoring less relevant information to reduce the cognitive processing costs. Together, our results have implications for both the design and interpretation of conjoint experiments.

We provide an equilibrium analysis of the efficiency properties of simultaneous bilateral tariff negotiations in a three-country model of international trade. We consider the setting in which discriminatory tariffs are allowed, and we utilize the “Nash-in-Nash” solution concept of Horn and Wolinsky (1988). We allow for a general family of political-economic country welfare functions and assess efficiency relative to these welfare functions. We establish a sense in which the resulting tariffs are inefficient and too low, so that excessive liberalization occurs from the perspective of the three countries.

In recent years, some prominent scholars have been making a surprising claim: examining literary texts for hidden depths is overblown, misguided, or indeed downright dangerous. Such examination, they've warned us, may lead to the loss of world Heidegger warned of (Gumbrecht), to the world-denying metaphysics Nietzsche warned of (Nehamas), or to the suspicious form of hermeneutics Ricoeur warned of (Best, Marcus, Moi). This paper seeks to suggest that, though the concerns are understandable, there's ultimately nothing to worry about. The fact that Nietzsche himself happily used metaphors of surface and depth suggests that they are not, in fact, metaphysically fraught. The fact that it's possible to appreciate surfaces at the same time as depths means that there's no real danger of losing the world. And as for depth-talk turning us into suspicious hermeneuts, that would happen only if we made two fallacious assumptions: first, that all depths are meanings; second, that all hidden features are in a text by accident. But since plenty of authors hide things deliberately, and since what's hidden often has nothing to do with propositional content, both assumptions are profoundly mistaken. Meanwhile, the surface/depth metaphor is the only thing that adequately captures the phenomenology of reading, especially when it comes to misdirection-based, hermetic, enigmatic, ironic, or satirical texts, where special activity on our part prompts a sudden leap to a radically different mode of understanding. And unlike its rivals, the surface/depth metaphor reflects a real asymmetry: depths explain surfaces, but not vice versa; surfaces are available without depths, but not the other way around. We need the metaphor, and we need to stay open to hidden depths as we read. As long as we don't come in with terrible assumptions, nothing bad will happen to us, and plenty of good things will. It's perfectly safe to go back in the water.

What explains the sharp divide in European public attitudes toward Grexit? We explore this question using original surveys from four of the largest European economies. We contend that differences in economic self-interest, and the often-mentioned chasm between supporters of mainstream and extremist parties, provide little insight into the public divide over Grexit. Instead, we show that the key factor is the split between the left and the right. We then develop and test a set of theoretical explanations for the prominence of this cleavage. We find that the left-right divide over Grexit is not driven by differences in attitudes on redistribution, levels of empathy, or general European Union support. Instead, left and right voters seem to have different expectations about how a default and exit of a currency-union member would affect the European economy. These expectations likely reflect differences in core beliefs about the consequences of a free-market approach.

• The first volume that brings together the work of scholars pioneering digital approaches to the Republic of Letters
• Innovative applications of social network analysis and digital methods to provide new perspectives on the Republic of Letters
• Exciting new perspectives on the European networks that made up the Republic of Letters
• A volume that elegantly combines traditional humanistic inquiry with innovative digital methods, to offer fresh perspectives on some of the most important issues in eighteenth-century studies
• This volume provides exemplary models of how social network analysis (SNA) can be adapted for historical research

This review evaluates the use of spatial models for the analysis of policymaking. First, we examine spatial theory and its applications in a variety ofinstitutional settings. We discuss how the preferences of the actors involvedin political processes, the steps in those processes, and the locations of thereversion policies affect the policies that emerge from the processes. To illustrate this and analyze how the rights of political actors determine the extentof policy reform and the occurrence of gridlock, we use a spatial model ofEuropean Union (EU) policy making. We apply the model to major EU reforms in two resource policy areas: the Common Agricultural Policy reformsof the past two decades and the recent reforms of the Emissions TradingSystem.

A growing number of countries have established programs to attract immigrants who can contribute to their economy. Research suggests that an immigrant's initial arrival location plays a key role in shaping their economic success. Yet immigrants currently lack access to personalized information that would help them identify optimal destinations. Instead, they often rely on availability heuristics, which can lead to the selection of sub-optimal landing locations, lower earnings, elevated outmigration rates, and concentration in the most well-known locations. To address this issue and counteract the effects of cognitive biases and limited information, we propose a data-driven decision helper that draws on behavioral insights, administrative data, and machine learning methods to inform immigrants' location decisions. The decision helper provides personalized location recommendations that reflect immigrants' preferences as well as data-driven predictions of the locations where they maximize their expected earnings given their profile. We illustrate the potential impact of our approach using backtests conducted with administrative data that links landing data of recent economic immigrants from Canada's Express Entry system with their earnings retrieved from tax records. Simulations across various scenarios suggest that providing location recommendations to incoming economic immigrants can increase their initial earnings and lead to a mild shift away from the most populous landing destinations. Our approach can be implemented within existing institutional structures at minimal cost, and offers governments an opportunity to harness their administrative data to improve outcomes for economic immigrants.